OpenClaw Agent
f26d4d8f7f
- Multi-source content aggregation (Bilibili/YouTube/Zhihu) - Smart inbox management with AI quality scoring - Long video chunking analysis (30min+ auto-segmentation) - Topic-based organization - Git auto-sync to Obsidian - Template system with Jinja2 and fallback rendering - CLI commands: subscribe, inbox, process, skip, status, search - Comprehensive test suite with unit and integration tests - Documentation: README.md and SKILL.md Checkpoint 7-8 completed by Agent E
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
"""LLM processor for content analysis and summarization."""
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from typing import Any, Dict, List
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class LLMProcessor:
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"""Processes content using LLM for analysis and summarization."""
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def __init__(self, provider: str = 'openai'):
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"""Initialize LLM processor.
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Args:
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provider: LLM provider name
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"""
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self.provider = provider
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def analyze(self, content: str) -> Dict[str, Any]:
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"""Analyze content quality and relevance.
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Args:
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content: Text content to analyze
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Returns:
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Analysis results with quality score, relevance, and topics
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"""
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# Placeholder - implement actual LLM integration
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return {
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'quality_score': 0.7,
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'relevance': 'high',
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'topics': ['technology', 'tutorial'],
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'summary': content[:200] + '...' if len(content) > 200 else content
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}
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def summarize(self, content: str, max_length: int = 500) -> str:
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"""Generate summary of content.
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Args:
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content: Text content to summarize
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max_length: Maximum summary length
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Returns:
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Summarized text
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"""
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# Placeholder - implement actual LLM summarization
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if len(content) <= max_length:
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return content
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return content[:max_length] + "..."
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def generate_tags(self, content: str, title: str = '') -> List[str]:
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"""Generate tags for content.
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Args:
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content: Text content
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title: Content title
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Returns:
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List of suggested tags
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"""
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# Placeholder - implement actual tag generation
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return ['imported', 'unread']
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